In this study, a bridge damage identification method based on the Long Short-Term Memory (LSTM) network and the contact point response is proposed, which utilizes the time behavior of the contact point response. The contact point response serving as the input to the LSTM network is obtained by calculating the vehicle-bridge interaction motion equation. A new bridge damage indicator titled the Euclidean Distance Damage Index (EDDI) is developed based on the difference between the actual and predicted values of the contact point response. Under ideal road surface conditions, the EDDI calculated in the healthy state of the bridge remains below 0.16, while exceeding 1.0 in the damaged state. When road roughness is considered, the EDDI is calculated to be less than 0.12 in the healthy state of the bridge and higher than 0.65 in the damaged state. The results show that the EDDI is more effective in distinguishing between the damaged and the healthy states of the bridge. Meanwhile, road roughness has a negative effect on the damage sensitivity of EDDI.

本研究提出了一种基于长短期记忆（LSTM）网络和接触点响应的桥梁损伤识别方法，该方法利用接触点响应的时间行为。通过计算车桥交互运动方程获得作为 LSTM 网络输入的接触点响应。一种名为欧几里德距离损伤指数（EDDI）的新桥梁损伤指标是根据接触点响应的实际值和预测值之间的差异开发的。在理想路面条件下，桥梁健康状态下计算的EDDI保持在0.16以下，而损坏状态下则超过1.0。当考虑路面不平度时，计算出桥梁健康状态下的EDDI小于0.12，损坏状态下的EDDI高于0.65。结果表明，EDDI能够更有效地区分桥梁的受损状态和健康状态。同时，道路粗糙度对EDDI的损伤敏感性有负面影响。